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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning Fundamentals | 20% | - Unsupervised learning
|
| Topic 2: OCI Data Science Service | 30% | - Model catalog
|
| Topic 3: Model Development and Deployment | 30% | - Model deployment
|
| Topic 4: MLOps and OCI Integration | 20% | - Automation and pipelines
|
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NEW QUESTION # 119
You are a data scientist working for a manufacturing company. You have developed a forecasting model to predict the sales demand in the upcoming months. You created a model artifact that contained custom logic requiring third-party libraries. When you deployed the model, it failed to run because you did not include all the third-party dependencies in the model artifact. What file should be modified to include the missing libraries?
Answer: D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Specify third-party libraries for model deployment.
Understand Artifacts: runtime.yaml defines runtime; score.py handles logic.
Evaluate Options:
A: Not a standard file—incorrect.
B: Inference code—not for dependencies.
C: Defines conda env with dependencies—correct.
D: Pip list—not used in OCI conda deployments.
Reasoning: runtime.yaml points to a conda env with all libraries.
Conclusion: C is correct.
OCI documentation states: “In runtime.yaml, specify the conda environment slug (e.g., ENVIRONMENT_SLUG: custom_env) containing all third-party libraries required by the model.” score.py (B) is for logic, requirements.txt (D) isn’t OCI-standard, and A doesn’t exist—C fixes the issue.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment - runtime.yaml".
NEW QUESTION # 120
Select two reasons why it is important to rotate encryption keys when using Oracle Cloud Infrastructure (OCI) Vault to store credentials or other secrets.
Answer: B,E
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify two reasons for key rotation in OCI Vault.
Understand Key Rotation: Enhances security by updating keys.
Evaluate Options:
A: Five-key limit—False, no such restriction.
B: Efficiency—False, not the purpose.
C: Reuse—False, rotation prevents reuse.
D: Reduces risk—True, limits exposure—correct.
E: Limits data—True, reduces breach scope—correct.
Reasoning: D and E are security-focused—key Vault benefits.
Conclusion: D and E are correct.
OCI documentation states: “Key rotation in Vault (D) reduces risk if a key is compromised and (E) limits the data encrypted by a single key version, enhancing security.” A, B, and C misrepresent rotation’s purpose—only D and E align with OCI’s Vault best practices.
1: Oracle Cloud Infrastructure Vault Documentation, "Key Rotation Benefits".
NEW QUESTION # 121
You want to use ADSTuner to tune the hyperparameters of a supported model you recently trained. You have just started your search and want to reduce the computational cost as well as assess the quality of the model class that you are using. What is the most appropriate search space strategy to choose?
Answer: B
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Select an ADSTuner strategy to minimize cost and assess model quality.
Understand ADSTuner: Optimizes hyperparameters with configurable search spaces.
Evaluate Options:
A: Detailed—Exhaustive, high cost—incorrect.
B: No search space—False; tuning requires a space.
C: Perfunctory—Quick, low-cost assessment—correct.
D: Dictionary—Defines space but not a strategy.
Reasoning: Perfunctory balances cost and initial quality check.
Conclusion: C is correct.
OCI documentation states: “ADSTuner’s perfunctory strategy (C) performs a quick, low-cost search to assess model quality, ideal for initial tuning.” Detailed (A) is costly, B misstates requirements, and D is a method, not a strategy—only C fits the goal.
1: Oracle Cloud Infrastructure ADS SDK Documentation, "ADSTuner Search Strategies".
NEW QUESTION # 122
Where do calls to stdout and stderr from score.py go in a model deployment?
Answer: A
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Locate score.py output in OCI model deployment.
Understand Deployment: Logs are centralized in OCI Logging.
Evaluate Options:
A: VM file—Not default; requires custom config—incorrect.
B: Predict log in OCI Logging—Standard destination—correct.
C: Cloud Shell—Separate tool, not logs—incorrect.
D: Console—UI, not raw logs—incorrect.
Reasoning: B aligns with OCI’s logging integration.
Conclusion: B is correct.
OCI documentation states: “score.py stdout and stderr are captured in the predict log within OCI Logging service (B), configured during deployment.” A isn’t standard, C and D don’t receive logs—only B fits OCI’s logging setup.
1: Oracle Cloud Infrastructure Data Science Documentation, "Model Deployment Logging".
NEW QUESTION # 123
Which two statements are true about published conda environments?
Answer: B,D
Explanation:
Detailed Answer in Step-by-Step Solution:
Objective: Identify true statements about published conda environments in OCI.
Understand Published Conda: Custom envs shared via Object Storage.
Evaluate Options:
A: False—Users create them, not OCI-curated.
B: True—odsc conda init sets up access to published envs.
C: False—Object Storage, not notebook, is the source.
D: False—Can create from scratch, not just modifications.
E: True—Conda env vars extend job configs.
Reasoning: B configures access, E enhances flexibility—match OCI behavior.
Conclusion: B and E are correct.
OCI documentation states: “odsc conda init (B) configures the bucket for published conda environments, and Jobs can use conda environment variables (E) alongside service vars.” A misattributes curation, C misplaces source, and D limits creation—only B and E are true per OCI’s conda management.
1: Oracle Cloud Infrastructure Data Science Documentation, "Published Conda Environments".
NEW QUESTION # 124
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